CSCE 420 - Artificial Intelligence

Fall 2014

Professor: Dr. Thomas R. Ioerger
Office: 322C Bright Bldg.
Phone: 458-5518
email:ioerger@cs.tamu.edu
office hours: Mon, 3:00-4:00

TA: Eric Nelson
email:ejn8411@tamu.edu
office: RDMC-B021
office hours: Wednesday 2pm - 4pm

Meeting: TR, 2:20-3:35, HRBB 113

Course Web Page: http://www.cs.tamu.edu/faculty/ioerger/cs420-fall14/index.html

Course Description (from TAMU course catalog): Basic concepts and methods of artificial intelligence; Heuristic search procedures for general graphs; game playing strategies; resolution and rule based deduction systems; knowledge representation; reasoning with uncertainty.

Prerequisites: CSCE 315 (Programming Studio)

Textbook

Russell, S. and Norvig, P. (2009). Artificial Intelligence: A Modern Approach. 3rd edition (blue cover). Prentice Hall.

Course Objectives

  1. To learn about intelligent search methods and their role in building complex problem-solving programs.
    1. to learn how to formulate computational problems as search
    2. to learn how various search algorithms work
    3. to learn their computational properties (space- and time-complexity)
    4. to learn how heuristics can improve efficiency of search
  2. To learn about knowledge representation techniques and methods for knowledge-based/intelligent decision-making in programs.
    1. to learn syntax and semantics of propositional logic and first-order logic
    2. to learn how inference algorithms work
    3. to learn the advantages of alternative knowledge respresentation systems
    4. to learn how to represent and reason about uncertainty using Bayesian probability
  3. To gain exposure to traditional sub-fields of AI (automated deduction, planning, machine learning, natural language...).
    1. to learn how symbolic planning algorithms work
    2. to learn different decision-making architectures for intelligent agents
    3. to learn how machine learning can be used to generalize from experience/examples
Topics Assignments, Projects, Exams, and Grading

The work for this course will consist of a mix of homeworks, programming assignments, and exams. The overall score for the course will be a weighted combination of these three components, which is tentatively set as follows:

The final grade will be determined from the weighted-average score as follows:

The penalty for late assignments is -5% per day (pro-rated over 24 hours).
After 10 days late, the deductions cease; the maximum loss of points is 50%. As long as you turn an assignment in by the end of the semester, it could still be worth as much as half-credit. This is to encourage you to eventually complete the assignment, even if you can't get it in on time initially.

Schedule:

assigmenttopicconceptsreading
Tues, Sept 2(first day of class)What is AI?perspectives on AI read Ch. 1
Thurs, Sept 4Intelligent Agentsdecision-making; architectures; core concepts in AIread Ch. 2
Tues, Sept 9Search AlgorithmsDFS, BFS, greedyread Ch. 3 (skip 3.5.3)
Thurs, Sept 11uniform cost, iterative deepening
Tues, Sept 16heuristics, A*
Thurs, Sept 18Program 1 due (BFS),
ATM.graph data file,
robot motion planning 		Optimizationhill-climbing, simulated annealingread Sec 4.1
Tues, Sept 23 Constraint Satisfactionbacktracking, heuristicsread Ch. 6
Thurs, Sept 25 AC-3, MACbacktracking alg, AC-3, and MAC
Tues, Sept 30 Program 2 due
(DFS, GREEDY) 		guest lecture by:
Dr. Yoonsuck Choe, 		Intro to Machine Learning
Thurs, Oct 2 Game Searchminimax algorithmread Ch. 5
Tues, Oct 7 alpha/beta pruning, board evaluation functions
Thurs, Oct 9 Program 3 due
(A*, Block-stacking)		Propositional Logicsyntax, semanticsread Ch. 7
Tues, Oct 14 inference proceduresnatural deduction, forward chaining
Thurs, Oct 16backward chaining, resolution backchaining algorithm,
example of NatDed and Res
Tues, Oct 21 Program 4 due
texas-cities.dat
TourOfTexas mapping tool 		satisfiability, DPLL, WalkSATslides on DPLL
Thurs, Oct 23 First-Order Logicquantifiers, model theory read Ch. 8
Tues, Oct 28 using FOL (concept representation; translation); ontologiesSec. 12.1-12.2
Thurs, Oct 30Event CalculusSec 12.3
Sat, Nov 1Program 5 due (CSP)due Sat by midnight
Tues, Nov 4 unification, inference proceduresread Ch. 9
Thurs, Nov 6 (continued)
Tues, Nov 11 Program 6 due graph.interconnect3
criss cross example 		Prolog mini-tutorial on Prolog
Thurs, Nov 13Default Reasoning non-monotonic logics, Semantic Nets, negation-as-failure in PrologSec 12.6
Tues, Nov 18 Probability Bayes Rule, Bayesian networks, MDPsCh. 13
Thurs, Nov 20Reasoning about action Situation Calculus; Frame ProblemSec 10.4.2
Tues, Nov 25 Planning algorithmsPDDL (STRIPS); goal regressionSec. 10.1-10.2; see also Sec 3.2 of (Weld, 1994)
Thurs, Nov 27(class cancelled)(Thanksgiving)
Tues, Dec 2HW1 duemore discussion of planning
Thurs, Dec 4other types of plannersSec 11.1-11.2
Tues, Dec 9HW2 due(last day of class)review for final
Wed, Dec 17final exam, 1:00-3:00

Academic Integrity Statement and Policy

Aggie Code of Honor: An Aggie does not lie, cheat or steal, or tolerate those who do.
see: Honor Council Rules and Procedures

Americans with Disabilities Act (ADA) Policy Statement

The Americans with Disabilities Act (ADA) is a federal anti-discrimination statute that provides comprehensive civil rights protection for persons with disabilities. Among other things, this legislation requires that all students with disabilities be guaranteed a learning environment that provides for reasonable accommodation of their disabilities. If you believe you have a disability requiring an accommodation, please contact Disability Services, in Cain Hall, Room B118, or call 845-1637. For additional information visit http://disability.tamu.edu.

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